Flying™ RTK Solution as Effective Enhancement of Conventional Float RTK
Д. В. Козлов, Gleb Zyryanov
Abstract
Д. В. Козлов, Gleb Zyryanov
Abstract
Float RTK is used to deliver sub-meter to decimeter accuracy for various L1 and L1&L2 RTK applications. Typically Float RTK insures sub-meter accuracy with start-up and decimeter level accuracy after few (or few tens) minutes. For given hardware, the RTK convergence time and steady state accuracy are subject of local environmental conditions (shading, multipath), baseline length and RTK algorithm itself. In given paper, we describe new RTK algorithm from Magellan: Flying RTK. Being quite a simple in realization and not so time consuming, it demonstrates statistically better performance compared to standard Float RTK. While Flying RTK algorithm can be applied to both L1 and L1/L2 systems, in given article we make accent on L1 RTK systems. We give apple-to-apple performance comparison between Float RTK and Flying RTK with the data collected in different environments and baselines. The results prove that CEP convergence to decimeter accuracy can be achieved 1.5-5 times (depending on conditions) faster when applying Flying RTK algorithm instead of Float RTK The data used for validation were collected with different Magellan receivers.
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Float RTK is used to deliver sub-meter to decimeter accuracy for various L1 and L1&L2 RTK applications. Typically Float RTK insures sub-meter accuracy with start-up and decimeter level accuracy after few (or few tens) minutes. For given hardware, the RTK convergence time and steady state accuracy are subject of local environmental conditions (shading, multipath), baseline length and RTK algorithm itself. In given paper, we describe new RTK algorithm from Magellan: Flying RTK. Being quite a simple in realization and not so time consuming, it demonstrates statistically better performance compared to standard Float RTK. While Flying RTK algorithm can be applied to both L1 and L1/L2 systems, in given article we make accent on L1 RTK systems. We give apple-to-apple performance comparison between Float RTK and Flying RTK with the data collected in different environments and baselines. The results prove that CEP convergence to decimeter accuracy can be achieved 1.5-5 times (depending on conditions) faster when applying Flying RTK algorithm instead of Float RTK The data used for validation were collected with different Magellan receivers.
Key concepts: Float (project management), Computer science, Metre, Convergence (economics), Real Time Kinematic, Multipath propagation, Real-time computing, Remote sensing